Updated: 2020-09-04 06:47:49 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
Rhode Island 1.46 20308 143
West Virginia 1.38 10822 197
Kentucky 1.30 53759 952
Tennessee 1.25 156760 1839
Pennsylvania 1.24 141414 866
Nebraska 1.22 35215 366
Florida 1.15 636015 3866
Maine 1.15 4613 29
Montana 1.15 7812 146
New York 1.14 441750 757
Arizona 1.13 203669 572
Ohio 1.13 127039 1242
Delaware 1.12 17375 78
New Jersey 1.12 194431 350
Idaho 1.11 33329 329
Louisiana 1.11 150404 795
Missouri 1.11 79935 1267
Oklahoma 1.11 60977 805
South Dakota 1.07 14153 295
Utah 1.06 53355 404
New Hampshire 1.05 7338 22
Maryland 1.04 110450 562
North Dakota 1.04 12526 253
Virginia 1.04 97258 745
South Carolina 1.03 121649 908
Kansas 1.02 44818 619
New Mexico 1.02 25756 134
Minnesota 1.01 77864 776
Wisconsin 1.01 78596 693
Washington 1.00 79075 482
Arkansas 0.98 61898 547
Mississippi 0.98 85026 652
North Carolina 0.98 172869 1569
California 0.97 726968 4971
Wyoming 0.97 3929 32
Connecticut 0.96 53136 141
Georgia 0.96 258334 2067
Illinois 0.96 241275 1811
Texas 0.95 654488 4423
Indiana 0.94 98908 909
Massachusetts 0.94 127602 309
Oregon 0.94 27339 208
Vermont 0.93 1631 7
Colorado 0.91 58627 278
Michigan 0.89 114919 694
Alabama 0.86 129514 1081
Iowa 0.82 67635 797
Nevada 0.79 70350 350

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Whitman WA 18 1 1.3 669 1380 56
Chelan WA 10 2 1.4 1801 2380 20
Clark WA 8 3 1.3 2687 580 31
Pierce WA 3 4 1.1 7655 890 50
Spokane WA 5 5 1.1 5497 1100 48
Douglas WA 11 6 1.4 1184 2860 10
Grant WA 9 7 1.1 2458 2590 34
King WA 1 8 0.8 19938 920 89
Snohomish WA 4 9 1.0 7176 910 34
Franklin WA 7 11 1.0 4126 4550 15
Yakima WA 2 12 0.9 11737 4710 21
Benton WA 6 20 0.6 4364 2250 10
OR
county ST case rank severity R_e cases cases/100k daily cases
Wasco OR 20 1 3.2 210 810 1
Josephine OR 22 2 2.2 162 190 2
Lane OR 8 3 1.4 747 200 11
Multnomah OR 1 4 0.9 6134 770 41
Washington OR 3 5 1.0 3875 670 30
Umatilla OR 4 6 1.1 2740 3560 18
Marion OR 2 7 0.9 3926 1170 33
Clackamas OR 5 11 0.9 2003 490 16
Jackson OR 7 12 0.9 850 400 13
Malheur OR 6 13 0.8 1218 4000 14
Deschutes OR 9 20 0.7 697 390 2
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Santa Cruz CA 31 1 1.8 1879 690 40
Tulare CA 13 2 1.3 14547 3160 145
Los Angeles CA 1 3 0.9 245002 2430 1082
Butte CA 30 4 1.4 2269 1000 79
Santa Clara CA 11 5 1.2 17975 940 224
Glenn CA 41 6 1.8 453 1620 6
Riverside CA 2 7 1.0 53689 2250 338
San Diego CA 5 8 1.1 39493 1200 283
Fresno CA 7 10 1.0 25758 2630 254
Alameda CA 8 11 1.1 18870 1150 202
San Bernardino CA 4 13 0.9 48528 2270 316
Sacramento CA 9 15 0.9 18672 1240 245
Orange CA 3 18 0.9 49423 1560 268
Kern CA 6 24 0.9 29745 3370 134

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
La Paz AZ 14 1 3.2 506 2440 3
Maricopa AZ 1 2 1.3 134871 3170 374
Pinal AZ 4 3 1.1 9735 2320 53
Cochise AZ 11 4 1.4 1838 1460 5
Mohave AZ 6 5 1.0 3671 1780 17
Yavapai AZ 10 6 1.1 2339 1040 10
Graham AZ 13 7 1.1 740 1950 8
Pima AZ 2 8 0.7 21405 2100 58
Coconino AZ 7 9 1.0 3356 2390 10
Navajo AZ 5 10 0.9 5632 5180 10
Santa Cruz AZ 9 11 1.1 2765 5940 3
Apache AZ 8 12 0.9 3354 4690 4
Yuma AZ 3 13 0.6 12278 5910 13
CO
county ST case rank severity R_e cases cases/100k daily cases
Lake CO 35 1 3.3 81 1070 0
San Miguel CO 32 2 2.2 94 1180 0
Boulder CO 7 3 1.2 2358 730 15
El Paso CO 4 4 1.0 6182 900 36
Adams CO 3 5 0.9 7732 1560 46
Jefferson CO 5 6 1.0 4882 860 26
Larimer CO 9 7 1.0 2013 600 19
Arapahoe CO 2 9 0.8 8347 1310 35
Denver CO 1 11 0.8 11374 1640 35
Weld CO 6 12 1.0 4113 1390 16
Douglas CO 8 14 0.9 2152 650 16
UT
county ST case rank severity R_e cases cases/100k daily cases
Millard UT 14 1 3.4 148 1160 2
Cache UT 6 2 1.6 2167 1770 18
Weber UT 4 3 1.4 3397 1370 36
Uintah UT 16 4 1.9 104 290 2
Salt Lake UT 1 5 1.0 24496 2190 151
Utah UT 2 6 1.0 11302 1910 106
Davis UT 3 7 1.1 4033 1180 44
Washington UT 5 8 1.3 2858 1780 17
San Juan UT 9 9 1.5 670 4380 1
Tooele UT 8 13 0.9 708 1090 5
Summit UT 7 16 0.7 857 2120 3
NM
county ST case rank severity R_e cases cases/100k daily cases
Taos NM 18 1 2.3 117 360 0
Otero NM 7 2 2.0 1128 1720 2
Doña Ana NM 4 3 1.4 2924 1360 26
Eddy NM 13 4 1.4 533 930 14
Chaves NM 9 5 1.2 772 1180 16
McKinley NM 2 6 1.2 4230 5810 8
Lincoln NM 17 7 1.5 173 890 1
Bernalillo NM 1 9 0.9 5892 870 26
Lea NM 6 10 1.0 1169 1670 12
Santa Fe NM 8 13 0.8 837 560 5
San Juan NM 3 16 0.6 3202 2510 3
Sandoval NM 5 19 0.5 1258 890 3

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Burlington NJ 12 1 1.5 6525 1460 29
Bergen NJ 1 2 1.2 21972 2360 41
Monmouth NJ 8 3 1.3 10904 1750 26
Ocean NJ 7 4 1.2 11359 1920 38
Hudson NJ 3 5 1.3 20367 3050 20
Passaic NJ 5 6 1.1 18582 3690 29
Union NJ 6 7 1.2 17324 3130 19
Camden NJ 9 10 1.0 9283 1830 24
Essex NJ 2 11 1.0 20642 2600 27
Middlesex NJ 4 15 0.9 18707 2260 19
PA
county ST case rank severity R_e cases cases/100k daily cases
Centre PA 29 1 2.3 563 350 28
Clinton PA 51 2 2.4 145 370 3
York PA 12 3 1.7 3580 810 70
Philadelphia PA 1 4 1.4 34273 2180 163
Lancaster PA 6 5 1.4 6911 1280 57
Butler PA 24 6 1.7 814 440 10
Northumberland PA 26 7 1.6 723 780 14
Allegheny PA 4 11 1.1 10531 860 67
Montgomery PA 2 13 1.1 11182 1360 48
Delaware PA 3 14 1.1 10546 1870 47
Bucks PA 5 17 1.1 7894 1260 32
Chester PA 8 18 1.1 5762 1110 29
Berks PA 7 19 1.1 6166 1480 34
Lehigh PA 9 22 1.2 5240 1450 13
MD
county ST case rank severity R_e cases cases/100k daily cases
Montgomery MD 2 1 1.2 20312 1950 86
Baltimore MD 3 2 1.1 15831 1910 112
Worcester MD 14 3 1.5 854 1660 13
Cecil MD 15 4 1.6 809 790 7
Anne Arundel MD 5 5 1.2 8539 1500 60
Prince George’s MD 1 6 1.0 26922 2970 104
Carroll MD 10 7 1.5 1726 1030 10
Harford MD 8 8 1.1 2553 1020 25
Howard MD 6 9 1.1 4462 1420 23
Baltimore city MD 4 13 0.8 14648 2380 49
Frederick MD 7 15 0.8 3540 1420 14
Charles MD 9 17 0.8 2427 1540 11
VA
county ST case rank severity R_e cases cases/100k daily cases
Montgomery VA 27 1 2.2 643 660 52
Grayson VA 57 2 2.1 225 1420 10
Westmoreland VA 55 3 2.4 229 1300 2
Surry VA 86 4 2.1 81 1230 4
Richmond VA 44 5 2.2 332 3740 1
Mecklenburg VA 32 6 1.7 547 1770 10
Prince George VA 29 7 1.6 587 1550 14
Chesterfield VA 5 8 1.3 5180 1530 40
Fairfax VA 1 13 1.0 18692 1630 95
Prince William VA 2 15 1.0 11019 2410 62
Newport News city VA 9 16 1.1 2397 1330 28
Loudoun VA 4 20 1.0 6081 1580 34
Virginia Beach city VA 3 24 0.9 6124 1360 35
Henrico VA 6 25 0.9 4708 1450 32
Norfolk city VA 7 34 0.8 4451 1810 21
Arlington VA 8 51 0.7 3589 1550 13
WV
county ST case rank severity R_e cases cases/100k daily cases
Monongalia WV 2 1 2.2 1243 1180 37
Fayette WV 6 2 1.7 387 880 29
Lincoln WV 26 3 2.1 123 580 2
Harrison WV 13 4 1.9 277 410 4
Brooke WV 30 5 2.0 97 430 2
Kanawha WV 1 6 1.4 1555 840 42
Wayne WV 14 7 1.6 266 650 5
Cabell WV 4 11 1.3 562 590 8
Mercer WV 9 12 1.3 323 530 6
Raleigh WV 7 13 1.2 378 500 6
Jefferson WV 8 16 1.1 371 660 5
Logan WV 5 17 1.0 505 1490 6
Berkeley WV 3 19 0.9 812 720 4
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 1.2 8356 1510 57
Kent DE 3 2 1.1 2655 1520 11
Sussex DE 2 3 0.9 6364 2900 10

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Cleburne AL 66 1 1.6 224 1500 6
Jefferson AL 1 2 1.0 16950 2570 162
Houston AL 15 3 1.2 2184 2090 41
Marengo AL 41 4 1.5 658 3370 5
Madison AL 4 5 1.1 6654 1860 56
Bullock AL 47 6 1.5 564 5450 4
Calhoun AL 12 7 1.1 2679 2330 37
Shelby AL 7 8 0.9 4781 2260 69
Lee AL 6 9 0.8 4944 3100 107
Montgomery AL 3 17 0.8 8115 3580 39
Mobile AL 2 18 0.8 12413 2990 52
Baldwin AL 8 21 0.8 4638 2230 35
Tuscaloosa AL 5 28 0.6 6059 2940 54
Marshall AL 9 54 0.6 3647 3830 7
MS
county ST case rank severity R_e cases cases/100k daily cases
Perry MS 68 1 2.8 326 2710 11
George MS 43 2 1.9 699 2950 6
Lafayette MS 12 3 1.3 1578 2950 47
Oktibbeha MS 14 4 1.3 1521 3070 27
Bolivar MS 13 5 1.3 1577 4840 27
Tallahatchie MS 47 6 1.5 652 4540 7
Smith MS 59 7 1.5 483 3010 4
Forrest MS 9 8 1.2 2189 2900 19
Lee MS 7 10 1.1 2267 2670 28
DeSoto MS 2 11 1.0 4756 2700 43
Harrison MS 3 19 1.0 3406 1680 33
Jackson MS 4 22 0.9 3099 2180 28
Rankin MS 6 34 0.8 2906 1920 19
Madison MS 5 35 0.8 3029 2930 18
Jones MS 8 37 0.9 2190 3200 10
Hinds MS 1 40 0.6 6591 2730 20
LA
county ST case rank severity R_e cases cases/100k daily cases
Plaquemines LA 48 1 2.8 608 2600 29
Orleans LA 3 2 1.5 11732 3010 74
St. Tammany LA 7 3 1.5 6203 2460 52
Lincoln LA 37 4 1.8 935 1970 9
Iberville LA 26 5 1.8 1349 4090 5
Lafayette LA 4 6 1.5 8357 3480 30
East Feliciana LA 28 7 1.3 1259 6460 56
East Baton Rouge LA 2 8 1.2 13984 3150 74
Jefferson LA 1 10 1.1 16629 3820 55
Caddo LA 5 13 1.1 7535 3030 40
Tangipahoa LA 9 15 1.2 4106 3150 20
Ouachita LA 8 17 1.2 5554 3560 23
Calcasieu LA 6 37 0.8 7463 3730 15

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Baker FL 48 1 2.2 1221 4390 21
Leon FL 21 2 1.8 6783 2350 148
Duval FL 6 3 1.5 27106 2930 205
Miami-Dade FL 1 4 1.1 160103 5900 847
Broward FL 2 5 1.3 72700 3810 412
Wakulla FL 53 6 1.7 932 2920 13
Orange FL 5 7 1.2 36694 2780 223
Polk FL 9 10 1.2 17411 2600 131
Hillsborough FL 4 11 1.1 37849 2740 216
Palm Beach FL 3 14 1.0 42684 2950 198
Pinellas FL 7 24 1.0 20165 2110 74
Lee FL 8 31 0.9 18966 2640 84
GA
county ST case rank severity R_e cases cases/100k daily cases
Lanier GA 128 1 2.6 273 2630 6
Clarke GA 15 2 1.7 3284 2640 120
Chattahoochee GA 44 3 1.6 1317 12230 54
Fannin GA 95 4 1.8 481 1930 11
Bulloch GA 29 5 1.3 2212 2960 80
Berrien GA 103 6 1.7 396 2080 5
Walton GA 38 7 1.4 1546 1720 23
Gwinnett GA 2 13 0.9 24748 2740 141
DeKalb GA 4 14 1.0 16816 2260 83
Cobb GA 3 17 0.9 17352 2330 119
Chatham GA 6 19 1.1 7230 2520 54
Fulton GA 1 23 0.9 25315 2480 129
Hall GA 5 24 1.0 7954 4060 80
Muscogee GA 9 32 1.1 5545 2820 26
Richmond GA 8 39 0.9 6157 3060 44
Clayton GA 7 71 0.6 6598 2370 34

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Coryell TX 51 1 2.2 1522 2020 138
Brewster TX 150 2 2.8 202 2190 3
Lubbock TX 17 3 2.0 8236 2730 234
Duval TX 116 4 2.4 351 3090 26
Tyler TX 153 5 2.6 189 880 4
Webb TX 11 6 1.8 11573 4250 223
Yoakum TX 160 7 2.4 160 1870 5
Dallas TX 2 12 1.1 75876 2930 439
Harris TX 1 14 0.9 109046 2370 840
Bexar TX 3 21 1.0 47064 2440 180
Tarrant TX 4 26 0.9 42291 2090 211
El Paso TX 8 50 0.8 20715 2470 90
Travis TX 6 53 0.8 26713 2220 71
Cameron TX 7 60 0.6 21452 5090 118
Hidalgo TX 5 64 0.5 28050 3300 162
Nueces TX 9 77 0.6 19081 5290 60
OK
county ST case rank severity R_e cases cases/100k daily cases
Atoka OK 46 1 2.9 150 1080 14
Craig OK 47 2 2.1 138 950 6
Muskogee OK 5 3 1.5 1494 2160 104
Kay OK 33 4 1.7 337 750 8
Payne OK 7 5 1.4 1304 1600 48
Tulsa OK 2 6 1.2 13701 2130 145
Adair OK 29 7 1.6 479 2170 10
Cleveland OK 3 9 1.2 4098 1480 59
Oklahoma OK 1 11 1.0 13794 1760 113
Wagoner OK 9 20 1.0 1189 1530 13
Rogers OK 6 35 0.9 1347 1480 11
Canadian OK 4 38 0.8 1570 1150 11
Comanche OK 8 46 0.6 1285 1050 12

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Ottawa MI 9 1 1.9 2351 830 57
Houghton MI 57 2 2.2 92 250 6
Lenawee MI 14 3 1.9 1383 1400 25
Marquette MI 37 4 1.9 259 390 5
Clinton MI 27 5 1.5 516 660 8
Shiawassee MI 30 6 1.4 451 660 9
Ingham MI 12 7 1.3 1833 630 16
Oakland MI 2 8 0.9 18347 1470 90
Wayne MI 1 10 0.8 31625 1800 111
Washtenaw MI 6 11 1.1 3483 950 20
Macomb MI 3 13 0.8 13270 1530 72
Kent MI 4 17 0.8 8618 1340 41
Jackson MI 7 21 1.1 2585 1630 9
Saginaw MI 8 27 0.8 2584 1340 18
Genesee MI 5 33 0.8 4004 980 11
WI
county ST case rank severity R_e cases cases/100k daily cases
Adams WI 49 1 2.1 130 650 6
Juneau WI 40 2 1.9 248 940 13
Door WI 48 3 2.1 137 500 2
Ozaukee WI 16 4 1.5 980 1110 15
Vilas WI 52 5 1.7 119 550 4
Crawford WI 54 6 1.8 110 680 2
Kewaunee WI 42 7 1.6 197 970 6
Dane WI 3 9 1.1 5628 1060 56
Outagamie WI 7 12 1.1 2021 1090 47
Kenosha WI 6 13 1.3 3017 1790 16
Milwaukee WI 1 18 0.8 24373 2550 88
Brown WI 4 23 0.8 5554 2140 48
Rock WI 8 25 1.0 1899 1170 18
Waukesha WI 2 31 0.8 5682 1420 33
Walworth WI 9 32 1.0 1743 1690 13
Racine WI 5 36 0.8 4500 2300 18

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Chippewa MN 46 1 2.3 146 1220 4
Clay MN 16 2 1.8 931 1480 17
Winona MN 20 3 1.6 539 1060 32
Dakota MN 3 4 1.1 6196 1480 94
Scott MN 7 5 1.3 2097 1460 30
Blue Earth MN 10 6 1.3 1341 2020 32
Hennepin MN 1 7 0.9 23570 1910 161
Stearns MN 5 9 1.2 3348 2130 28
Ramsey MN 2 11 0.9 9394 1730 68
Anoka MN 4 12 1.0 4874 1400 48
Washington MN 6 13 1.0 3118 1230 44
Olmsted MN 8 21 1.0 2074 1350 14
Nobles MN 9 47 0.7 1875 8590 3
SD
county ST case rank severity R_e cases cases/100k daily cases
Brookings SD 7 1 1.6 400 1170 30
Todd SD 23 2 2.0 83 820 1
Minnehaha SD 1 3 1.2 5456 2920 61
Codington SD 8 4 1.4 374 1340 20
Lincoln SD 3 5 1.2 954 1740 17
Clay SD 6 6 1.1 420 3020 24
Pennington SD 2 7 0.9 1538 1410 39
Brown SD 4 11 1.0 720 1850 15
Meade SD 9 15 0.6 329 1200 9
Beadle SD 5 19 0.6 643 3500 2
ND
county ST case rank severity R_e cases cases/100k daily cases
Stark ND 4 1 1.4 802 2600 35
Stutsman ND 8 2 1.5 246 1170 14
Cass ND 1 3 1.2 3650 2100 41
Williams ND 7 4 1.3 484 1420 17
Morton ND 5 5 1.2 696 2280 18
Walsh ND 12 6 1.7 147 1360 2
Burleigh ND 2 7 0.9 2124 2270 36
Grand Forks ND 3 9 0.7 1604 2280 40
Benson ND 9 10 1.2 242 3510 4
Ward ND 6 14 0.7 530 770 9

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
New Haven CT 2 1 1.1 13762 1600 29
Fairfield CT 1 2 0.9 19005 2010 42
Middlesex CT 6 3 1.3 1471 900 5
New London CT 5 4 1.1 1600 600 9
Hartford CT 3 5 0.8 13603 1520 39
Windham CT 8 6 1.2 812 700 4
Litchfield CT 4 7 1.0 1723 940 6
Tolland CT 7 8 1.0 1160 770 6
MA
county ST case rank severity R_e cases cases/100k daily cases
Bristol MA 6 1 1.2 9857 1760 32
Suffolk MA 2 2 0.9 23630 2980 74
Barnstable MA 9 3 1.4 1869 870 6
Middlesex MA 1 4 0.9 27784 1740 60
Essex MA 3 5 0.9 18972 2430 46
Worcester MA 4 6 1.0 14240 1730 28
Norfolk MA 5 7 1.0 11072 1590 20
Plymouth MA 7 8 0.9 9665 1890 19
Hampden MA 8 9 1.0 7961 1700 15
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.5 17072 2690 116
Newport RI 4 2 1.7 455 550 6
Washington RI 3 3 1.5 720 570 7
Kent RI 2 4 1.2 1703 1040 11
Bristol RI 5 5 1.0 357 730 2

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Otsego NY 42 1 2.8 206 340 19
Tompkins NY 33 2 2.3 294 290 10
Cortland NY 50 3 2.3 102 210 1
St. Lawrence NY 34 4 2.1 284 260 3
New York City NY 1 5 1.1 239808 2840 287
Nassau NY 3 6 1.3 44915 3310 77
Suffolk NY 2 7 1.3 44998 3020 59
Erie NY 7 8 1.2 10081 1100 69
Rockland NY 5 9 1.4 14328 4430 25
Westchester NY 4 15 1.1 37102 3830 41
Orange NY 6 18 1.1 11490 3040 14
Monroe NY 8 24 0.9 5496 740 15
Dutchess NY 9 32 0.8 4891 1660 10

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Franklin VT 4 1 2.2 123 250 0
Chittenden VT 1 2 1.1 805 500 3
Rutland VT 2 3 1.1 124 210 2
Bennington VT 5 4 1.1 105 290 1
Windsor VT 7 5 0.9 80 140 0
Windham VT 3 6 0.1 123 290 0
Addison VT 6 7 0.0 81 220 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Penobscot ME 4 1 1.4 238 160 3
York ME 2 2 1.0 866 430 11
Cumberland ME 1 3 1.1 2204 760 5
Androscoggin ME 3 4 0.9 620 580 2
Kennebec ME 5 5 0.9 193 160 1
NH
county ST case rank severity R_e cases cases/100k daily cases
Hillsborough NH 1 1 1.4 4069 990 11
Strafford NH 4 2 1.5 394 310 3
Merrimack NH 3 3 1.0 507 340 2
Rockingham NH 2 4 0.7 1822 600 4
Cheshire NH 5 5 0.5 132 170 1
Belknap NH 6 6 0.7 126 210 0
Carroll NH 8 7 0.5 106 220 0
Grafton NH 7 8 0.2 114 130 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Spartanburg SC 6 1 1.3 5197 1720 60
Abbeville SC 43 2 1.7 449 1820 9
Cherokee SC 30 3 1.7 815 1440 8
Pickens SC 16 4 1.4 2292 1870 34
Richland SC 3 5 1.1 11631 2850 168
Marlboro SC 32 6 1.5 761 2800 16
York SC 10 7 1.2 4387 1700 43
Lexington SC 5 8 1.2 5907 2060 47
Florence SC 9 9 1.2 4390 3170 40
Greenville SC 2 13 0.9 12305 2470 59
Charleston SC 1 14 0.8 14250 3610 71
Beaufort SC 8 16 1.0 4845 2650 27
Horry SC 4 22 0.9 9461 2950 28
Berkeley SC 7 25 0.8 4892 2340 22
NC
county ST case rank severity R_e cases cases/100k daily cases
New Hanover NC 14 1 1.6 3208 1430 54
Yancey NC 95 2 1.9 98 550 2
Mecklenburg NC 1 3 1.0 26075 2470 166
Cabarrus NC 13 4 1.3 3298 1640 40
Johnston NC 9 5 1.3 4005 2090 43
Columbus NC 41 6 1.4 1150 2040 14
Watauga NC 67 7 1.5 469 870 12
Cumberland NC 6 9 1.2 4224 1270 54
Guilford NC 3 11 1.1 7084 1350 69
Wake NC 2 18 0.8 15583 1490 136
Union NC 8 20 1.0 4101 1810 38
Gaston NC 7 25 1.0 4199 1940 36
Forsyth NC 5 31 0.9 6297 1690 40
Durham NC 4 39 0.9 7044 2300 32

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Silver Bow MT 11 1 2.6 118 340 2
Cascade MT 6 2 1.5 332 410 17
Carbon MT 17 3 1.8 90 850 1
Yellowstone MT 1 4 1.1 2200 1390 42
Rosebud MT 7 5 1.1 315 3410 18
Flathead MT 4 6 1.0 646 660 16
Gallatin MT 2 7 1.1 1113 1060 8
Big Horn MT 3 9 1.0 692 5170 10
Lake MT 8 10 1.4 207 700 2
Missoula MT 5 12 0.7 443 380 2
Lewis and Clark MT 9 13 1.0 199 300 1
WY
county ST case rank severity R_e cases cases/100k daily cases
Albany WY 11 1 2.2 153 400 6
Park WY 9 2 2.0 171 590 2
Washakie WY 13 3 1.7 108 1330 0
Uinta WY 4 4 1.3 305 1480 2
Lincoln WY 12 5 1.3 115 600 1
Natrona WY 6 6 1.0 301 370 3
Laramie WY 2 7 1.0 580 590 3
Campbell WY 7 8 0.8 202 420 3
Sweetwater WY 5 9 0.9 305 690 1
Teton WY 3 11 0.6 435 1890 2
Fremont WY 1 12 0.4 611 1520 2
Carbon WY 8 13 0.7 192 1240 0
ID
county ST case rank severity R_e cases cases/100k daily cases
Bingham ID 10 1 2.5 599 1320 39
Gem ID 22 2 2.8 223 1310 4
Bannock ID 6 3 1.9 789 930 30
Washington ID 17 4 1.6 293 2920 5
Twin Falls ID 5 5 1.3 1726 2060 15
Ada ID 1 6 1.0 11423 2560 72
Bonneville ID 4 7 1.1 1919 1710 32
Kootenai ID 3 10 1.0 2233 1450 15
Jerome ID 8 11 1.2 620 2650 6
Canyon ID 2 12 0.8 7343 3460 39
Payette ID 7 14 0.9 714 3100 13
Blaine ID 9 26 0.5 607 2760 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Jefferson OH 54 1 2.4 284 420 5
Butler OH 7 2 1.6 4438 1170 134
Muskingum OH 53 3 2.1 310 360 4
Ashland OH 73 4 2.2 174 330 2
Montgomery OH 5 5 1.4 6340 1190 162
Guernsey OH 77 6 2.1 137 350 2
Franklin OH 1 7 1.2 22370 1750 190
Hamilton OH 3 8 1.2 11476 1410 99
Lucas OH 4 15 1.1 6579 1520 51
Cuyahoga OH 2 18 0.9 15988 1280 88
Summit OH 6 26 1.0 4585 850 41
Marion OH 8 45 1.1 3013 4610 3
Mahoning OH 9 65 0.6 2875 1240 6
IL
county ST case rank severity R_e cases cases/100k daily cases
McDonough IL 54 1 2.0 246 800 14
McLean IL 14 2 1.5 2183 1260 151
Morgan IL 30 3 1.9 553 1610 16
Greene IL 69 4 2.0 131 990 6
Ogle IL 31 5 1.9 523 1020 9
Kankakee IL 12 6 1.5 2385 2150 38
Cook IL 1 7 0.8 128129 2450 542
Winnebago IL 7 12 1.2 4345 1520 32
Kane IL 5 14 1.0 11443 2160 60
Madison IL 8 21 1.0 4227 1590 60
St. Clair IL 6 23 1.0 6046 2290 60
DuPage IL 2 26 0.9 14854 1590 86
Will IL 4 34 0.7 11808 1710 74
Lake IL 3 35 0.8 14777 2100 61
McHenry IL 9 48 0.8 3990 1300 25
IN
county ST case rank severity R_e cases cases/100k daily cases
Delaware IN 16 1 1.5 1345 1160 59
Miami IN 49 2 1.8 338 940 6
Monroe IN 17 3 1.3 1278 880 46
Montgomery IN 41 4 1.6 453 1180 9
Vigo IN 14 5 1.3 1416 1310 43
Howard IN 19 6 1.5 1118 1360 12
Marion IN 1 7 1.0 19006 2010 128
Hamilton IN 6 12 0.9 4088 1290 50
Allen IN 5 13 0.9 5142 1390 45
Hendricks IN 8 15 1.0 2423 1510 23
Elkhart IN 3 17 1.0 5774 2840 30
Lake IN 2 21 0.8 9334 1920 49
Vanderburgh IN 7 31 0.8 2655 1460 23
St. Joseph IN 4 42 0.5 5304 1970 45
Johnson IN 9 48 0.7 2098 1380 9

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Wayne TN 16 1 3.3 1828 10980 431
Sequatchie TN 85 2 3.1 159 1080 6
Moore TN 91 3 2.5 107 1690 5
Van Buren TN 93 4 2.3 100 1750 9
Smith TN 59 5 2.3 538 2760 8
Fayette TN 29 6 1.9 946 2380 22
Carter TN 31 7 1.8 919 1630 31
Knox TN 5 16 1.2 7118 1560 114
Wilson TN 8 23 1.3 2935 2210 33
Hamilton TN 3 27 1.1 8289 2320 82
Rutherford TN 4 32 1.1 7995 2600 54
Davidson TN 2 37 0.9 26234 3840 83
Sumner TN 7 39 1.1 4181 2330 30
Williamson TN 6 48 0.9 4464 2040 29
Shelby TN 1 53 0.6 27913 2980 101
Bradley TN 9 69 0.8 2531 2420 15
KY
county ST case rank severity R_e cases cases/100k daily cases
Carter KY 72 1 2.5 129 470 4
Jefferson KY 1 2 1.6 13320 1740 317
Nelson KY 29 3 2.1 398 880 16
Mercer KY 74 4 2.2 128 590 4
Larue KY 80 5 2.1 108 760 3
Greenup KY 43 6 1.8 230 640 10
Union KY 75 7 1.8 127 860 8
Fayette KY 2 8 1.2 6030 1890 116
Madison KY 6 9 1.3 1165 1300 46
Kenton KY 4 14 1.3 1768 1070 17
Warren KY 3 17 1.1 3467 2740 46
Daviess KY 7 26 1.2 1022 1020 12
Shelby KY 9 35 1.2 922 1970 6
Hardin KY 8 43 1.0 960 890 11
Boone KY 5 46 1.0 1326 1030 9

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Audrain MO 42 1 2.2 294 1140 10
Polk MO 36 2 1.9 362 1150 12
Dunklin MO 26 3 1.8 543 1780 15
Boone MO 6 4 1.4 2987 1690 127
Adair MO 46 5 1.9 256 1010 8
Gentry MO 75 6 2.1 100 1500 2
Webster MO 48 7 1.7 255 670 12
Jackson MO 4 8 1.2 5774 830 82
St. Louis MO 1 9 1.0 20061 2010 205
Greene MO 5 10 1.1 3686 1280 112
St. Louis city MO 2 13 1.2 6304 2030 43
St. Charles MO 3 14 1.0 5977 1530 70
Jefferson MO 7 16 1.1 2958 1320 52
Jasper MO 8 17 1.2 1702 1430 26
Clay MO 9 28 1.1 1437 600 18
AR
county ST case rank severity R_e cases cases/100k daily cases
Washington AR 2 1 1.8 7129 3120 83
Prairie AR 64 2 2.3 117 1420 1
Newton AR 63 3 2.0 129 1640 4
Franklin AR 55 4 1.8 177 1000 4
Lawrence AR 45 5 1.8 256 1540 2
Miller AR 26 6 1.5 632 1440 9
Benton AR 3 7 1.2 5497 2120 45
Craighead AR 6 8 1.2 1957 1850 30
Pulaski AR 1 9 0.9 7258 1840 61
Sebastian AR 4 19 0.9 2941 2310 21
Faulkner AR 8 21 0.8 1743 1420 18
Pope AR 7 24 0.8 1811 2850 17
Jefferson AR 5 27 0.8 2159 3070 18
Hot Spring AR 9 38 0.9 1731 5160 4

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1820.9 seconds to compute.
2020-09-04 07:18:10

version history

Today is 2020-09-04.
107 days ago: Multiple states.
99 days ago: \(R_e\) computation.
96 days ago: created color coding for \(R_e\) plots.
91 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
91 days ago: “persistence” time evolution.
84 days ago: “In control” mapping.
84 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
76 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
71 days ago: Added Per Capita US Map.
69 days ago: Deprecated national map.
65 days ago: added state “Hot 10” analysis.
60 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
58 days ago: added per capita disease and mortaility to state-level analysis.
46 days ago: changed to county boundaries on national map for per capita disease.
41 days ago: corrected factor of two error in death trend data.
37 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
32 days ago: added county level “baseline control” and \(R_e\) maps.
28 days ago: fixed normalization error on total disease stats plot.
21 days ago: Corrected some text matching in generating county level plots of \(R_e\).
15 days ago: adapter knot spacing for spline.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.